ReviewEuropean radiology2025
Figure plagiarism and manipulation, an under-recognised problem in academia.
Review in European radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Academic writing in medicine and healthcare.Frontiers in medicine · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Academic plagiarism undermines the integrity of scientific research. While text-based plagiarism detection tools are widely used, the rise of artificial intelligence (AI) has introduced new challenges, particularly in text and image generation and manipulation. We briefly discuss the evolving landscape of plagiarism and the innovations that have come about with the proliferation of AI, focusing on the implications for text and image manipulation in academic writing and research. We discuss some of the current tools and practices used to detect AI-generated and manipulated text and images, including plagiarism detection software, computer vision algorithms, and manual reverse image searches. AI can enhance manuscript readability but also facilitates plagiarism and bias reinforcement due to the material it is trained on. Text-based detection tools are adapting to AI-generated content, yet image-based detection lags behind. Though tools to detect AI manipulation show promise, they are not perfect, particularly for manipulated images. Simple reverse image searches are a promising tool and can sometimes identify plagiarized figures that have undergone limited manipulation, but human oversight is often necessary. We believe that integrating image fabrication, manipulation and plagiarism detection into standard fraud detection packages is essential to uphold academic integrity in the new world of AI. Enhanced vigilance and technology are critical, particularly in fields like medical imaging, where image authenticity directly impacts research and thus clinical outcomes. KEY POINTS: We discuss the problems related to the rise of AI with regard to image manipulation in academic work, and how radiology is particularly at risk. We shed light on the rarely and little discussed topic of AI image manipulation and outright fraud. We hope to incite further discussion and adoption of image fraud prevention software. We discuss the use of some tools which are gradually becoming adopted and how some journals have begun to screen for image manipulation and fraud. We suggest an easy technique of using reverse image search that can sometimes be extremely useful despite its simplicity and can be easily adapted into researchers' practice.
Indexed as
Identifiers
39939423What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.